Overview

Dataset statistics

Number of variables9
Number of observations3000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory211.1 KiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

Sensor_beta is highly correlated with Sensor_alpha_plusHigh correlation
Sensor_alpha_plus is highly correlated with Sensor_betaHigh correlation
Sensor_beta is highly correlated with Sensor_alpha_plusHigh correlation
Sensor_alpha_plus is highly correlated with Sensor_betaHigh correlation
Sensor_gamma is highly correlated with Sensor_alpha_plusHigh correlation
Sensor_alpha_plus is highly correlated with Sensor_gammaHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Sensor_alpha has unique values Unique
Sensor_beta has unique values Unique
Sensor_gamma has unique values Unique
Sensor_alpha_plus has unique values Unique
Sensor_beta_plus has unique values Unique
Sensor_gamma_plus has unique values Unique
Minutes has 42 (1.4%) zeros Zeros

Reproduction

Analysis started2022-05-10 14:59:35.244873
Analysis finished2022-05-10 14:59:56.223258
Duration20.98 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8499.5
Minimum7000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2022-05-10T16:59:56.357114image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum7000
5-th percentile7149.95
Q17749.75
median8499.5
Q39249.25
95-th percentile9849.05
Maximum9999
Range2999
Interquartile range (IQR)1499.5

Descriptive statistics

Standard deviation866.1697293
Coefficient of variation (CV)0.1019083157
Kurtosis-1.2
Mean8499.5
Median Absolute Deviation (MAD)750
Skewness0
Sum25498500
Variance750250
MonotonicityStrictly increasing
2022-05-10T16:59:56.820330image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70001
 
< 0.1%
90031
 
< 0.1%
89941
 
< 0.1%
89951
 
< 0.1%
89961
 
< 0.1%
89971
 
< 0.1%
89981
 
< 0.1%
89991
 
< 0.1%
90001
 
< 0.1%
90011
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
70001
< 0.1%
70011
< 0.1%
70021
< 0.1%
70031
< 0.1%
70041
< 0.1%
70051
< 0.1%
70061
< 0.1%
70071
< 0.1%
70081
< 0.1%
70091
< 0.1%
ValueCountFrequency (%)
99991
< 0.1%
99981
< 0.1%
99971
< 0.1%
99961
< 0.1%
99951
< 0.1%
99941
< 0.1%
99931
< 0.1%
99921
< 0.1%
99911
< 0.1%
99901
< 0.1%

Hour
Real number (ℝ≥0)

Distinct23
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.59233333
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2022-05-10T16:59:57.112678image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q119
median21
Q321
95-th percentile22
Maximum23
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.507169985
Coefficient of variation (CV)0.179007264
Kurtosis11.89399048
Mean19.59233333
Median Absolute Deviation (MAD)1
Skewness-3.333721061
Sum58777
Variance12.3002413
MonotonicityNot monotonic
2022-05-10T16:59:57.330189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
211440
48.0%
19331
 
11.0%
18324
 
10.8%
20322
 
10.7%
22304
 
10.1%
2356
 
1.9%
720
 
0.7%
1618
 
0.6%
317
 
0.6%
815
 
0.5%
Other values (13)153
 
5.1%
ValueCountFrequency (%)
115
0.5%
29
0.3%
317
0.6%
411
0.4%
511
0.4%
614
0.5%
720
0.7%
815
0.5%
911
0.4%
1014
0.5%
ValueCountFrequency (%)
2356
 
1.9%
22304
 
10.1%
211440
48.0%
20322
 
10.7%
19331
 
11.0%
18324
 
10.8%
1712
 
0.4%
1618
 
0.6%
1514
 
0.5%
1411
 
0.4%

Minutes
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.79466667
Minimum0
Maximum59
Zeros42
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2022-05-10T16:59:57.522443image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.18474935
Coefficient of variation (CV)0.5968032047
Kurtosis-1.180180663
Mean28.79466667
Median Absolute Deviation (MAD)15
Skewness0.03956982245
Sum86384
Variance295.3156101
MonotonicityNot monotonic
2022-05-10T16:59:57.847260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3169
 
2.3%
169
 
2.3%
4568
 
2.3%
2066
 
2.2%
4164
 
2.1%
4964
 
2.1%
2560
 
2.0%
1459
 
2.0%
658
 
1.9%
258
 
1.9%
Other values (50)2365
78.8%
ValueCountFrequency (%)
042
1.4%
169
2.3%
258
1.9%
352
1.7%
449
1.6%
545
1.5%
658
1.9%
753
1.8%
848
1.6%
948
1.6%
ValueCountFrequency (%)
5947
1.6%
5841
1.4%
5751
1.7%
5634
1.1%
5547
1.6%
5449
1.6%
5346
1.5%
5241
1.4%
5150
1.7%
5042
1.4%

Sensor_alpha
Real number (ℝ)

UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.919117979
Minimum-342.2263886
Maximum361.7641601
Zeros0
Zeros (%)0.0%
Negative1518
Negative (%)50.6%
Memory size23.6 KiB
2022-05-10T16:59:58.071133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-342.2263886
5-th percentile-165.7459598
Q1-68.7988094
median-2.059706876
Q365.06219685
95-th percentile161.5591035
Maximum361.7641601
Range703.9905488
Interquartile range (IQR)133.8610063

Descriptive statistics

Standard deviation99.17117412
Coefficient of variation (CV)-51.67539214
Kurtosis-0.0689697149
Mean-1.919117979
Median Absolute Deviation (MAD)66.88891535
Skewness0.03367334274
Sum-5757.353936
Variance9834.921777
MonotonicityNot monotonic
2022-05-10T16:59:58.303820image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-72.083584871
 
< 0.1%
91.394481631
 
< 0.1%
79.288488921
 
< 0.1%
21.907222911
 
< 0.1%
154.18337521
 
< 0.1%
186.44616131
 
< 0.1%
-108.53118911
 
< 0.1%
10.202878181
 
< 0.1%
-175.02755051
 
< 0.1%
-48.364835761
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
-342.22638861
< 0.1%
-311.64667761
< 0.1%
-299.89539941
< 0.1%
-291.10660771
< 0.1%
-275.6099271
< 0.1%
-275.52799531
< 0.1%
-272.05067631
< 0.1%
-271.83563541
< 0.1%
-267.15340531
< 0.1%
-266.03550091
< 0.1%
ValueCountFrequency (%)
361.76416011
< 0.1%
320.20138071
< 0.1%
314.29772921
< 0.1%
302.17441081
< 0.1%
298.24534781
< 0.1%
287.17388761
< 0.1%
284.74434661
< 0.1%
281.36917481
< 0.1%
278.48049361
< 0.1%
275.41928351
< 0.1%

Sensor_beta
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-24.59531615
Minimum-612.7569858
Maximum430.2453697
Zeros0
Zeros (%)0.0%
Negative1763
Negative (%)58.8%
Memory size23.6 KiB
2022-05-10T16:59:58.588918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-612.7569858
5-th percentile-232.6568962
Q1-115.504386
median-31.30472733
Q364.7707356
95-th percentile201.5701991
Maximum430.2453697
Range1043.002356
Interquartile range (IQR)180.2751216

Descriptive statistics

Standard deviation132.6811441
Coefficient of variation (CV)-5.394569572
Kurtosis0.06477724278
Mean-24.59531615
Median Absolute Deviation (MAD)88.36508133
Skewness0.0398941903
Sum-73785.94844
Variance17604.286
MonotonicityNot monotonic
2022-05-10T16:59:58.869044image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11.434977441
 
< 0.1%
81.688252441
 
< 0.1%
173.11634351
 
< 0.1%
-22.538218611
 
< 0.1%
-126.83009311
 
< 0.1%
-65.571980751
 
< 0.1%
98.212627571
 
< 0.1%
187.35891821
 
< 0.1%
37.226916131
 
< 0.1%
-35.412376611
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
-612.75698581
< 0.1%
-476.28142781
< 0.1%
-454.50532151
< 0.1%
-435.79851031
< 0.1%
-421.65238741
< 0.1%
-413.06204411
< 0.1%
-408.39567051
< 0.1%
-408.31502411
< 0.1%
-402.69863621
< 0.1%
-400.78745111
< 0.1%
ValueCountFrequency (%)
430.24536971
< 0.1%
404.33310591
< 0.1%
386.35814391
< 0.1%
374.42730031
< 0.1%
370.098211
< 0.1%
359.01604091
< 0.1%
353.85496311
< 0.1%
338.68241851
< 0.1%
328.87116541
< 0.1%
326.59021441
< 0.1%

Sensor_gamma
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.080555762
Minimum-503.6016615
Maximum359.0697497
Zeros0
Zeros (%)0.0%
Negative1507
Negative (%)50.2%
Memory size23.6 KiB
2022-05-10T16:59:59.077684image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-503.6016615
5-th percentile-197.618675
Q1-82.92184947
median-0.7599613893
Q381.00505497
95-th percentile172.3328408
Maximum359.0697497
Range862.6714112
Interquartile range (IQR)163.9269044

Descriptive statistics

Standard deviation118.0882764
Coefficient of variation (CV)-19.42063867
Kurtosis0.1116788098
Mean-6.080555762
Median Absolute Deviation (MAD)81.83937389
Skewness-0.2703709451
Sum-18241.66729
Variance13944.84102
MonotonicityNot monotonic
2022-05-10T16:59:59.317240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-52.961742811
 
< 0.1%
-40.299662891
 
< 0.1%
219.70676771
 
< 0.1%
152.65503531
 
< 0.1%
30.4435561
 
< 0.1%
-93.911415041
 
< 0.1%
4.8227832231
 
< 0.1%
-21.336325831
 
< 0.1%
-204.3059191
 
< 0.1%
194.60707061
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
-503.60166151
< 0.1%
-442.85156191
< 0.1%
-433.42563621
< 0.1%
-419.53400461
< 0.1%
-417.98793621
< 0.1%
-415.12434091
< 0.1%
-402.45357881
< 0.1%
-401.67563241
< 0.1%
-387.58986811
< 0.1%
-380.57105971
< 0.1%
ValueCountFrequency (%)
359.06974971
< 0.1%
319.98727591
< 0.1%
310.54081211
< 0.1%
306.97314011
< 0.1%
302.65398611
< 0.1%
299.92473811
< 0.1%
295.46453581
< 0.1%
295.39008381
< 0.1%
294.49030491
< 0.1%
292.70495921
< 0.1%

Sensor_alpha_plus
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.05485006
Minimum-400.7216994
Maximum465.9461662
Zeros0
Zeros (%)0.0%
Negative1506
Negative (%)50.2%
Memory size23.6 KiB
2022-05-10T16:59:59.565138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-400.7216994
5-th percentile-178.6902501
Q1-77.78906076
median-0.8617628204
Q390.39878539
95-th percentile216.1466573
Maximum465.9461662
Range866.6678656
Interquartile range (IQR)168.1878461

Descriptive statistics

Standard deviation122.2482028
Coefficient of variation (CV)17.3282496
Kurtosis0.04168426148
Mean7.05485006
Median Absolute Deviation (MAD)82.68260396
Skewness0.2293033241
Sum21164.55018
Variance14944.62308
MonotonicityNot monotonic
2022-05-10T16:59:59.838743image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-72.696384891
 
< 0.1%
-53.733226941
 
< 0.1%
-64.032448021
 
< 0.1%
140.51829361
 
< 0.1%
-128.48350731
 
< 0.1%
15.220372421
 
< 0.1%
46.448660261
 
< 0.1%
-92.420498481
 
< 0.1%
317.78850561
 
< 0.1%
251.47879161
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
-400.72169941
< 0.1%
-393.12327761
< 0.1%
-371.77438881
< 0.1%
-365.22712161
< 0.1%
-342.39261021
< 0.1%
-328.82927291
< 0.1%
-313.99999611
< 0.1%
-310.40923781
< 0.1%
-308.16406771
< 0.1%
-302.93264411
< 0.1%
ValueCountFrequency (%)
465.94616621
< 0.1%
423.46540611
< 0.1%
420.24221191
< 0.1%
417.43078911
< 0.1%
416.73627071
< 0.1%
394.97876061
< 0.1%
385.27160441
< 0.1%
381.06819291
< 0.1%
380.56574091
< 0.1%
359.17413691
< 0.1%

Sensor_beta_plus
Real number (ℝ)

UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2364888196
Minimum-340.0022366
Maximum322.2359304
Zeros0
Zeros (%)0.0%
Negative1530
Negative (%)51.0%
Memory size23.6 KiB
2022-05-10T17:00:00.137510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-340.0022366
5-th percentile-161.8564647
Q1-68.913955
median-2.206641342
Q367.97689689
95-th percentile166.9794956
Maximum322.2359304
Range662.2381671
Interquartile range (IQR)136.8908519

Descriptive statistics

Standard deviation100.0832637
Coefficient of variation (CV)-423.2050542
Kurtosis-0.08154393495
Mean-0.2364888196
Median Absolute Deviation (MAD)68.30203647
Skewness0.01989113243
Sum-709.4664588
Variance10016.65967
MonotonicityNot monotonic
2022-05-10T17:00:00.369853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145.29922721
 
< 0.1%
-162.13971461
 
< 0.1%
73.001192461
 
< 0.1%
36.004193161
 
< 0.1%
-71.969206521
 
< 0.1%
-38.500796051
 
< 0.1%
-14.379676211
 
< 0.1%
-46.998820021
 
< 0.1%
-60.710932281
 
< 0.1%
105.44097471
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
-340.00223661
< 0.1%
-329.88455381
< 0.1%
-313.44054231
< 0.1%
-301.29568361
< 0.1%
-294.63209851
< 0.1%
-286.30282821
< 0.1%
-285.34723341
< 0.1%
-282.09318931
< 0.1%
-274.05424971
< 0.1%
-267.514961
< 0.1%
ValueCountFrequency (%)
322.23593041
< 0.1%
315.17079511
< 0.1%
311.42954211
< 0.1%
306.86821041
< 0.1%
292.31532431
< 0.1%
274.53690341
< 0.1%
273.18852571
< 0.1%
271.19784911
< 0.1%
269.37831221
< 0.1%
265.20045791
< 0.1%

Sensor_gamma_plus
Real number (ℝ)

UNIQUE

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.04004858
Minimum-340.9956572
Maximum370.8076238
Zeros0
Zeros (%)0.0%
Negative1455
Negative (%)48.5%
Memory size23.6 KiB
2022-05-10T17:00:00.577617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-340.9956572
5-th percentile-165.3727289
Q1-67.20441132
median3.432020443
Q369.03391447
95-th percentile164.1481965
Maximum370.8076238
Range711.8032809
Interquartile range (IQR)136.2383258

Descriptive statistics

Standard deviation100.899952
Coefficient of variation (CV)97.01465292
Kurtosis-0.004668333845
Mean1.04004858
Median Absolute Deviation (MAD)67.47499578
Skewness0.01031447578
Sum3120.145741
Variance10180.80032
MonotonicityNot monotonic
2022-05-10T17:00:00.808980image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-143.85624271
 
< 0.1%
-208.3202131
 
< 0.1%
121.02607091
 
< 0.1%
-88.684853411
 
< 0.1%
-4.8730416421
 
< 0.1%
-20.886706551
 
< 0.1%
-246.92506531
 
< 0.1%
-96.202316821
 
< 0.1%
108.99046871
 
< 0.1%
8.4261770771
 
< 0.1%
Other values (2990)2990
99.7%
ValueCountFrequency (%)
-340.99565721
< 0.1%
-306.35740761
< 0.1%
-294.3531811
< 0.1%
-286.46271581
< 0.1%
-281.54346741
< 0.1%
-279.7515871
< 0.1%
-273.49979261
< 0.1%
-272.13591651
< 0.1%
-265.87665581
< 0.1%
-262.94713531
< 0.1%
ValueCountFrequency (%)
370.80762381
< 0.1%
334.48754591
< 0.1%
333.03796991
< 0.1%
320.88943361
< 0.1%
311.29543491
< 0.1%
304.30367521
< 0.1%
304.25466471
< 0.1%
294.14163571
< 0.1%
289.43968421
< 0.1%
288.99852041
< 0.1%

Interactions

2022-05-10T16:59:53.117685image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:35.696062image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:37.559396image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:39.745314image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:42.175957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:44.224403image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:46.535436image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:48.580338image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:50.489774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:53.356908image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:35.879827image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:37.744445image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:40.019032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:42.560348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:44.418603image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:46.879876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:48.823134image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:50.666967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:53.639785image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:36.022359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:37.898789image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:40.242872image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:42.881263image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:44.599142image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:47.099793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:49.005898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:50.918254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:53.912066image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:36.249267image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:38.057819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:40.492188image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:43.095288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:44.860245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:47.542240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:49.279098image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:51.172450image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:54.177098image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:36.468419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:38.236719image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:40.769903image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:43.279434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:45.217314image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:47.694554image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:49.529933image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:51.478584image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:54.491252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:36.653825image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:38.422101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:41.000424image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:43.467705image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:45.497312image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:47.876996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:49.790504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:51.811063image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:54.740836image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:36.870738image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:38.585819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:41.239588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:43.646875image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:45.745239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:48.020781image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:49.973481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:52.130698image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:54.965987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:37.087657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:38.769406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:41.515562image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:43.801847image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:45.951301image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:48.185253image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:50.149538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:52.401273image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:55.199084image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:37.317657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:39.061781image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:41.797111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:43.984937image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:46.206897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:48.368780image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:50.315160image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-10T16:59:52.769843image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-05-10T17:00:00.952190image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-10T17:00:01.152128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-10T17:00:01.369150image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-10T17:00:01.592942image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-10T16:59:55.658560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-10T16:59:56.072372image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexHourMinutesSensor_alphaSensor_betaSensor_gammaSensor_alpha_plusSensor_beta_plusSensor_gamma_plus
070002131-72.083585-11.434977-52.961743-72.696385145.299227-143.856243
17001213-193.61998040.13708185.419244-26.266801-125.39123780.904973
27002223142.578382-51.613402125.055611182.136746-9.726951-114.913402
370032125-64.150951287.306752-70.96547313.78973838.187260137.761691
470041811193.750787-61.989533-110.347066-16.864786148.541916232.424616
57005215200.151155-69.828082-103.90927558.3071276.339606-58.217237
670061841-172.17684627.400690-142.223978-190.350320-79.866788104.104437
770072139164.427444-27.26667141.961835-19.597537-40.367741205.792899
87008223349.137676-219.450252-122.874167178.923309-51.770902-113.031012
970091828-21.098065-156.6374217.0432028.242528-29.71888058.156633

Last rows

df_indexHourMinutesSensor_alphaSensor_betaSensor_gammaSensor_alpha_plusSensor_beta_plusSensor_gamma_plus
29909990211217.67719088.932349106.609691-63.68084711.901239-62.050918
299199911930-31.941731-90.184404121.333344150.89955051.252914107.765113
2992999219382.172242-141.069527-41.133716188.303696-153.31029460.661858
299399931134237.639186-297.274989-172.872315216.866617-170.684348229.076958
299499941958110.5441148.203070-155.356768-8.573200-154.493904-156.344969
29959995326-101.91308387.33707777.193476-79.69403413.273965143.004786
2996999621652.424913-37.107249120.373905208.090551-135.567057-53.867634
29979997210-103.29513334.736979-195.018118-164.294768-26.9930646.063715
299899982013170.027159-73.100306-117.33065111.353824-3.366007-140.113661
299999991917-173.83923866.386097-141.968498197.558808-44.847981142.537872